In various organizations, companies, product and service centres and communities, agents (for example, customer services representatives) engage with customers in goal-directed dialogues for addressing concerns of the customers. Oftentimes, the agents carry out the goal-oriented dialogues in form of textual conversations, such as online chat conversations with the customers. For effectively achieving a target outcome of a textual conversation, various models have been developed to analyze mood of a customer during the textual conversation with an agent to get insights about the customer's intent and the likelihood of the conversation reaching the target outcome. However, conventional mood (or sentiment) mining techniques are only concerned with predicting an overall sentiment expressed in a body of text corresponding to the textual conversation. As a result, the conventional mood mining techniques are rendered inadequate for the purpose of deriving insights for achieving the target outcome in the goal-directed textual conversations.